library(dplyr)
library(tidyr)
library(tidytext)
library(ggplot2)
library(wordcloud)
library(ggwordcloud)
library(plotly)
library(lubridate)
library(leaflet)
library(tidygeocoder)
library(scales)
library(hexbin)
library(viridis)Airbnb Data Visualization
Read in the data & initial check
# read the data
airbnb <- read.csv("Airbnb_Open_Data.csv")
# factor all the characters
airbnb <- airbnb |> mutate(across(where(is.character), as.factor))# checking missing values
colSums(is.na(airbnb)) id NAME
0 0
host.id host_identity_verified
0 0
host.name neighbourhood.group
0 0
neighbourhood lat
0 8
long country
8 0
country.code instant_bookable
0 105
cancellation_policy room.type
0 0
Construction.year price
214 0
service.fee minimum.nights
0 409
number.of.reviews last.review
183 0
reviews.per.month review.rate.number
15879 326
calculated.host.listings.count availability.365
319 448
house_rules license
0 0 sum(duplicated(airbnb)) # there are 541 duplicated data[1] 541airbnb <- airbnb[-which(duplicated(airbnb)), ] # remove the duplicated rows
dim(airbnb) # there are 102058 rows and 26 variables[1] 102058 26Tidy the dataset
levels(airbnb$neighbourhood.group)[1] "" "Bronx" "brookln" "Brooklyn"
[5] "manhatan" "Manhattan" "Queens" "Staten Island"# rename the levels of neighbourhood group
airbnb <- airbnb |>
mutate(
neighbourhood.group = case_when(
neighbourhood.group %in% c("brookln") ~ "Brooklyn",
neighbourhood.group %in% c("manhatan") ~ "Manhattan",
TRUE ~ neighbourhood.group
),
neighbourhood.group = factor(neighbourhood.group)
)
levels(airbnb$neighbourhood.group)[1] "" "Bronx" "Brooklyn" "Manhattan"
[5] "Queens" "Staten Island"# The neighbourhoods with neighborhood group empty
airbnb |>
filter(neighbourhood.group == "") |>
pull(neighbourhood) [1] Washington Heights Clinton Hill East Village Upper East Side
[5] Woodside Williamsburg Bushwick Prospect Heights
[9] East Village Williamsburg Clinton Hill Chelsea
[13] Prospect Heights East Harlem Bushwick Eastchester
[17] Williamsburg Harlem Chinatown Williamsburg
[21] Queens Village Harlem Williamsburg Bedford-Stuyvesant
[25] East Village East Harlem Harlem Bushwick
[29] Upper West Side
225 Levels: Allerton Arden Heights Arrochar Arverne Astoria ... Woodside# group those area into the a county
lookup <- tibble::tribble(
~neighbourhood, ~borough,
"Washington Heights", "Manhattan",
"Clinton Hill", "Brooklyn",
"East Village", "Manhattan",
"Upper East Side", "Manhattan",
"Woodside", "Queens",
"Williamsburg", "Brooklyn",
"Bushwick", "Brooklyn",
"Prospect Heights", "Brooklyn",
"Chelsea", "Manhattan",
"East Harlem", "Manhattan",
"Eastchester", "Bronx",
"Harlem", "Manhattan",
"Chinatown", "Manhattan",
"Queens Village", "Queens",
"Bedford-Stuyvesant", "Brooklyn",
"Upper West Side", "Manhattan"
)airbnb <- airbnb |>
left_join(lookup, by = "neighbourhood") |>
mutate(
neighbourhood.group = if_else(
neighbourhood.group == "",
borough, # fill with correct borough
neighbourhood.group # keep existing value
),
neighbourhood.group = as.factor(neighbourhood.group)
) |>
select(-borough)
levels(airbnb$neighbourhood.group)[1] "Bronx" "Brooklyn" "Manhattan" "Queens"
[5] "Staten Island"# remove the $ in the price variable
airbnb$price <- gsub("\\$", "", airbnb$price)
airbnb$price <- as.numeric(airbnb$price)Visualizations
Count of Listings by Neighborhood Group
neighbour_list <- ggplot(airbnb, aes(x = neighbourhood.group))+
geom_bar(fill = "darkorchid", width = 0.6)+
labs(
title = "Number of Listings per Neighbourhood",
x = "Neighbourhood group",
y = "Count"
)+
theme_minimal()
ggplotly(neighbour_list)Average Price by Room Type
price_per_room <- airbnb |>
group_by(room.type) |>
summarise(avg_price= mean(price, na.rm = T))
price_roomtype <- ggplot(price_per_room, aes(x = room.type, y = avg_price)) +
geom_bar(stat = "identity", fill = "turquoise", width = 0.5)+
geom_text(aes(label = round(avg_price,2)))+
scale_y_continuous(labels = dollar_format())+
labs(
title = "Average Price by Room Type",
x = "Room Type",
y = "Average Price($)"
) +
theme_minimal()
ggplotly(price_roomtype)Listings by Price and Room Type
list_room_price <- ggplot(airbnb, aes(x = room.type,
y = neighbourhood.group,
fill= price
)) +
geom_tile()+
scale_fill_viridis(option = "plasma",
direction = -1,
)+
labs(
title = "Prices by Room Type & Neighborhood",
x = "Room Type",
y = "Neighborhood Group",
fill = "Price ($)"
)
ggplotly(list_room_price, tooltip = "text")Map
nyc <- map_data("county") |> filter(region == "new york")
label_df <- airbnb |>
group_by(neighbourhood.group) |>
summarise(
long = mean(long, na.rm = TRUE),
lat = mean(lat, na.rm = TRUE)
)
label_df <- airbnb |>
group_by(neighbourhood.group) |>
summarise(
long = mean(long, na.rm = TRUE),
lat = mean(lat, na.rm = TRUE),
n_listings = n(),
avg_price = mean(price, na.rm = TRUE)
)
map <- ggplot() +
geom_polygon(data = nyc, aes(x = long, y = lat, group = group),
fill = "white", color = "gray") +
coord_quickmap(xlim = c(-74.3, -73.7), ylim = c(40.49,40.9)) +
geom_hex(data = airbnb, aes(x = long, y = lat), bins = 1000) +
scale_fill_gradient(low = "skyblue", high = "red3") +
geom_text(data = label_df,
aes(x = long, y = lat,
label = neighbourhood.group,
text = paste(
"Total listings:", n_listings,
"\n Avg price: $", round(avg_price, 1)
)),
color = "black",
size = 3) +
labs(title = "Airbnb House Map") +
theme_classic()
map
ggplotly(map, tooltip = "text")Word Cloud
airbnb <- airbnb |>
mutate(house_rules = as.character(house_rules))
# word frequency table
word_freq <- airbnb |>
filter(house_rules != "") |> # remove missing house rules
unnest_tokens(bigram, house_rules, token = "ngrams", n = 2) |>
# lowercasing all; token the sentences into bigram(2 words for a phase)
separate(bigram, c("word1", "word2"), sep = " ") |> # separate the bigram
filter(
!is.na(word1), !is.na(word2), # remove NA
!word1 %in% stop_words$word | !word2 %in% stop_words$word
# remove the bigram with both words are stop words
) |>
unite(bigram, word1, word2, sep = " ") |> # recombine the bigram
count(bigram, sort = TRUE)
head(word_freq, 10) bigram n
1 no smoking 26067
2 no pets 11741
3 check in 10188
4 the house 8626
5 the apartment 7754
6 check out 7395
7 the building 6653
8 no parties 6311
9 be respectful 6142
10 smoking no 6137word_freq |> filter(n > 1300) |>
ggplot(aes(label = bigram, size = n, color = n)) +
geom_text_wordcloud() +
scale_size_area(max_size = 20) +
scale_color_gradient(low = "#0072B2", high = "#E69F00") +
theme_minimal()